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Supporting Case-Based Reasoning with Neural

Networks: An Illustration for Case Adaptation

David Leake

Xiaomeng

Y e and David

Crandall

Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA

Abstract

Case-based reasoning (CBR) is a knowledge-based reasoning and learning methodology that applies

prior cases-records of prior instances or experiences-by adapting their lessons to solve new problems.

The CBR process enables explainable reasoning from few examples, with minimal learning cost. How- ever, the success of CBR depends on having appropriate similarity and adaptation knowledge, which may be hard to acquire. This paper illustrates the opportunity to leverage neural network methods to reduce the knowledge engineering burden for case-based reasoning. It presents an experimental exam- ple from ongoing work on re?ning the case di?erence heuristic approach to learning case adaptation knowledge by applying neural network learning.

Keywords

case adaptation, case-based reasoning, knowledge acquisition, neural networks, hybrid systems

1. Introduction

Case-based reasoning (CBR) is a methodology for reasoning and learning in which agents rea- 1 2 3 4 5 ]. Amajorinspirationfor CBR models came from observations of human reasoning [ 2 6 ]. Human experts-and others- areremindedofpastexperiencesastheyencounternewproblems. Thesharingof"warstories" is a common way experts transmit knowledge. Motivations for applying CBR include easing knowledge acquisition, both because cases may be easier to elicit than rules [ 3 ] and because, in some domains, cases are captured routinely as a byproduct of other processes, providing a readily-available knowledge resource [ 7 ]. CBR also provides multiple choices for where to place domain knowledge, enabling knowledge engineers to focus knowledge capture e?ort wherever most convenient. CBR models have been developed for many knowledge-rich tasks and have been widely applied [ 8 9 10 11 However, even when case acquisition and engineering are straightforward, case-based rea- soningrequiresadditionalknowledgesourcesthatmaybedi?culttoacquire. Mostnotably,the knowledge used to adapt prior solutions to new circumstances is often captured in rule-based form and may be hard to generate. For many years, acquiring case adaptation knowledge has been seen as a key challenge for case-based reasoning [ 3 12

]. The di?culty of acquiring caseIn A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F. van Harmelen (Eds.), Proceedings of the AAAI

2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) - Stanford

University, Palo Alto, California, USA, March 22-24, 2021.

?0000-0002-8666-3416(D .Leake); 0000-0002-2289-1022 (X. Y e)©2021 Copyright for this paper by its authors.

Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).CEUR

Workshop

Proceedingshttp://ceur-ws.org

ISSN 1613-0073CEUR Workshop Proceedings (CEUR-WS.org) adaptation knowledge has led to numerous CBR applications that focus primarily on retrieval 13 14 ], functioning as extended memories for a human rather than as full problem-solvers. The di?culty of capturing adaptation knowledge has led to interest in how case adaptation knowledge can be learned. The most widely used approach, called the case di?erence heuristic 12 ], generates rules by comparing pairs of cases, ascribing di?erences in their solutions to di?erencesintheproblemstheyaddress. Themethodgeneratesnewrulesthatadjustsolutions analogously when a retrieved case di?ers from a new problem in a similar way. This approach has proven useful, but has depended on human expertise to de?ne problem characterizations and to determine how to generalize the observed di?erences. els by using a neural network to learn how to process a given di?erence. Following on seminal work by Liao, Liu and Chao [ 15 ], it implements a case di?erence heuristic model by using a neural network to learn how to process a given di?erence. However, rather than relying only on the di?erence, as in their work, our approach also provides the neural model with the problem context in which the adaptation is performed. We present experiments illustrating its bene?ts over both CBR baselines and a neural net baseline. Because an important bene?t of performance for such queries. The results support the bene?t of the approach in that setting. The paper ?rst highlights the complementary strengths of case-based reasoning and neural network methods, which make it appealing to achieve bene?ts from both by using network methods to support case-based reasoning. It then sketches the steps of the case-based reason- ing process, the sources of knowledge on which it depends, and the case di?erence heuristic approach to learning case adaptation knowledge. It next presents a preliminary case study on exploiting a neural network to determine solution di?erences for the case di?erence heuris- tic. Finally, it considers broader opportunities for synergies between case-based reasoning and network methods.

2. Complimentary Strengths of Case-Based Reasoning and

Network Methods

ods to support CBR. CBR is appealing because it can function successfully with very limited data, and because the ability to place knowledge in multiple "knowledge containers" (as de- scribed below) can facilitate development of knowledge-rich systems. In addition, it is a lazy learning method with inexpensive learning: CBR systems learn by simply storing new cases, without generalization until (and only if) needed to process a new problem. Neural network models, in contrast, do not easily exploit prior knowledge. They depend on expensive. However, they o?er the ability to achieve high performance in a knowledge light way. Thus they are promising for learning from data to support CBR. Figure 1:The CBR cycle. Image based on Aamodt and Plaza [1]

3. Case-Based Reasoning and Knowledge

3.1. The CBR Cycle

The CBR process is a cycle in which problems are presented to the system for processing steps often described asretrieve, reuse, revise, andretain. The most relevant prior case is retrieved, its retained-stored as a new case, learned by the system. The process is illustrated in Figure 1 The case-based reasoning process uses multiple forms of knowledge, commonly referred to astheCBRknowledgecontainers[ 16 ]: representationalvocabulary,caseknowledge,similarity knowledge, and case adaptation knowledge. The knowledge containers can be seen as over- lapping, in the sense that placing knowledge in one can decrease the need for knowledge in another. Forexample,increasingthecasebasesizecandecreasetheneedforadaptationknowl- edge, if the added cases enable retrieving cases more similar to incoming problems (which reduces the need for adaptation). The ability to choose where to place knowledge provides ?exibility for knowledge acquisition from humans and by automated learning methods.

3.2. Acquiring Case Adaptation Knowledge

Acquiring case adaptation knowledge is a classic hard problem for case-based reasoning. Case adaptation knowledge is often encoded in the form of rules, whose e?ectiveness may depend on quality of a domain theory. Early case-based reasoning research invested extensive e?ort to develop case adaptation knowledge (e.g., [ 17 ]). The di?culty of generating case adaptation knowledge was a serious impediment to the development of CBR systems with rich reasoning, and prompted development of case-based aiding systems which functioned as external mem- ories, retrieving cases to present to the user without adaptation [ 13 ]. Later work recognized the potential of learning methods to capture case adaptation knowledge. These included the generation of rules by decision tree learning [ 18 19 ], and the use of case-based reasoning for the adaptation process itself [ 20 21
22
23
The most in?uential adaptation rule learning approach is thecase-di?erence heuristic(CDH) approach. This knowledge-light approach generates adaptation knowledge using cases in the case base as data (e.g. [ 12 24
25
26
27
28
]). The case di?erence heuristic generates rules for adapting retrieved cases to ?t new problems, using cases in the case base. Given a pair of cases, it calculates the di?erence between their problem descriptions (generally represented as feature vectors) and the di?erence between their solutions (generally numerical values for regression tasks). From the pair, a rule is generated. The rule encodes that when an input problem and retrieved case have a problem di?erence similar to the one from which the rule solution di?erence. For example, in the real estate price prediction domain, a rule might be generated from two similar apartments, one a two-bedroom and the other a three-bedroom, to adjustthepricegivenanadditionalbedroom. Normally,humanknowledgeisusedtodetermine how the adjustment should be done (e.g., a ?xed or percent increment), and the process relies to future cases.

Liao, Liu and Chao [

15 ] have applied deep learning to learn di?erences to assign to the solu- tion of a retrieved case for regression problems. Their method presents the problem di?erence of two cases to a network which has been trained on pairs to output solution di?erences. Craw et al. [ 29
] showed that with careful tuning of feature weights, superior performance can be achieved by taking more context into account for the case di?erence heuristic. We are in- vestigating the use of network methods to avoid the tuning step when adding context to the case-di?erence heuristic approach.

4. An Illustration from Case Adaptation

we conducted an initial experiment. Liao, Liu and Chao [ 15 ] tested neural network adaptation for the NACA 0012 airfoil dataset [ 30
] from the UCI repository [ 31
]. Their results showed that neural networks can learn adaptations for a CDH approach in that domain. Our experiment compares ?ve di?erent systems: a k-NN system with?= 1, which can be seen as a CBR system with no case adaptation, a k-NN system with?= 3, which can be viewed as a CBR system with very simple adaptation (solution averaging), a CBR system using adaptation rules generated using the case di?erence heuristic ("normal CDH"), inspired by Craw et al. [ 29
], a CBR system using a neural network to learn rules from CDH and carry out adaptation ("network CDH"), and, as a further baseline for comparison, a NN system that solves the regression problem directly. The design of the network CDH system builds on the model of of Liao et al. [ 15 ], but dif- fers in two respects. First, in addition to taking as input the problem di?erences, it takes as input the problem of the retrieved case, which provides context for the adaptation. Second, in addition to being trained on pairs of similar cases, it is trained on pairs of random cases, enabling generation of rules for larger di?erences (cf. Jalali and Leake [ 32
]). Our experimental procedure di?ers from theirs in testing on data sets for which we restrict the available training cases so that the test query is always novel.

4.1. Implementations

Depending on the task domain, there is minor variation in the number of neurons per layer. The system is trained until the validation error converges. The CBR system with normal CDH is implemented following Craw et al. [ 29
]. A pair of cases is compared to produce an adaptation example, within which one of the two cases" prob- lem descriptions is used as a context, indicating that a problem di?erence in such a context can lead to such a solution di?erence. This system is denoted as "CBR + normal CDH" and implemented as follows:quotesdbs_dbs35.pdfusesText_40

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